Fusion application of event-triggered deep reinforcement learning and adaptive fuzzy PID in multi-physics disturbance suppression for ultra-precision motion control

This study introduces a hybrid control strategy that synergizes event-triggered deep reinforcement learning (DRL) with an adaptive fuzzy PID to address the challenges posed by multi-physics disturbances in ultra-precision motion systems. The proposed system employs an event-triggered mechanism that activates system updates only when control errors exceed preset thresholds, significantly reducing unnecessary computational loads. A Deep Q-Network (DQN) is integrated to autonomously optimize control policies through environment interactions, enabling intelligent adaptation to complex disturbances. Concurrently, an adaptive fuzzy PID controller dynamically adjusts proportional, integral, and derivative gains based on real-time error signals and disturbance intensity, effectively compensating for system nonlinearities and uncertainties. The synergy between DRL-based decision-making and fuzzy logic parameter tuning ensures coordinated responses to time-varying disturbances. Experimental validation demonstrates notable performance improvements, with response times consistently maintained at 3.4–3.7 ms and steady-state errors reduced to 0.003–0.006 μm under multi-physics interference. These metrics confirm the strategy’s capability to balance rapid response with micron-level precision while minimizing controller actuation frequency. The dual-layer optimization approach–combining intelligent event-triggered learning with model-free fuzzy adaptation – provides a scalable solution for high-precision motion control in environments with coupled physical disturbances, offering potential applications in semiconductor manufacturing and precision optics alignment systems.